Maximizing advertising performance

ABSTRACT

A target value of a performance metric associated with an advertising schedule can be obtained at an automated media content scheduling system. An upper boundary value indicating a value of the performance metric at which corrective action to decrease a predicted value of the performance metric is to be taken can be determined. A lower boundary value indicating a value of the performance metric at which corrective action to increase the predicted value of the performance metric is to be taken can also be determined. Information indicating the predicted value of the performance metric can be received at the automated media content scheduling system. The advertising schedule can be continually adjusted to maintain the predicted value of the performance metric between the upper boundary value and the lower boundary value.

CROSS REFERENCES TO RELATED APPLICATIONS

The present U.S. Utility patent application claims priority pursuant to35 U.S.C. §120 as a divisional of U.S. Utility application Ser. No.12/417,632 entitled “MAXIMIZING ADVERTISING PERFORMANCE,” filed Apr. 3,2009, which claims priority pursuant to 35 U.S.C. §119(e) to U.S.Provisional Application No. 61/064,916, entitled “SYSTEMS AND METHODSFOR PREDICTING AND ACHIEVING REQUIRED LEVELS OF ADVERTISINGPERFORMANCE,” filed Apr. 3, 2008, both of which are hereby incorporatedherein by reference in their entirety and made part of the present U.S.Utility patent application for all purposes.

FIELD

The present disclosure relates generally to multimedia, and moreparticularly to providing content related to commercial media programs.

BACKGROUND

Generally, advertisers want their advertising to reach a specific numberof people in a specific demographic category. Often, this number isdefined in a contract for the purchase of a requirement of theircontract to purchase advertising space with a media outlet. Gross RatingPoints (GRP) is one metric utilized to measure advertising performancethat results from an advertising campaign. GRP can be defined as arating achieved by a specific advertising campaign. Use of performancemetrics, such as GRP, assures the advertiser that its advertising willreach a specified number or people in a targeted demographic. GRP alsoallows an advertiser to compare the effectiveness of its advertisingdollars from different media outlets. From the perspective of theadvertising media outlet (e.g., television station, radio station,Internet, etc.), the media outlet can price its advertising inventorybased on a GRP (or other, similar metric) rate rather than a spot rate.

Current techniques that allow radio station and the advertiser topredict GRP performance for an advertising campaign can be cumbersome toimplement, and are therefore somewhat less than perfect.

SUMMARY

Historic data regarding gross rating points or other performancecriteria related to advertising parameters can be stored. Multiplefiltering criteria related to a consumer survey can be specified, andused to filter the historic data. In some embodiments, user inputrelating to consumer demographic information; is received, and thefiltering criteria can be set or adjusted based on the user input. Insome embodiments, only two filtering criteria are used. Using thefiltered historic data and the advertising parameters (time station,etc.), a predicted gross rating point value for a proposed advertisementcan be generated. The predicted gross rating point value can be updated,either periodically or in response to receiving new historical data, andan updated predicted gross rating point value can be generated toreflect the new data. In some embodiments, an advertising schedule canbe continually adjusted to maintain the predicted gross rating pointvalue between upper and lower boundary values.

Various embodiments provide a web page to display a plurality ofday-time combinations according to selectable scheduling criteria. Theweb page can display a predicted gross rating point value foradvertisements scheduled to be run at particular day-time combinations.One or more additional advertisements can be added at particularday-time combinations, and the displayed prediction of advertisingperformance can be updated accordingly.

In some embodiments, one or more contracts and a plurality ofunscheduled advertisements are provided. Each of the plurality ofadvertisements is associated with one of one or more contracts, each ofwhich can have a performance contractual value and a contractperformance value. The contract performance value of each contract canbe related to an advertisement performance value, which is in turn canbe associated with the individual advertisements that are part of acontract. One or more scheduling parameters, which can include a stationidentifier and a date, are selected, and advertisements can beautomatically scheduled based on the performance contractual value andthe contract performance value.

Scheduling the advertisements can include obtaining demographicinformation related to a consumer survey, establishing multiple criteriabased on the demographic information, selecting an advertisement to bescheduled, identifying one or more valid advertisement breaks for thenext advertisement and scoring each one of the one or more validadvertisement breaks. If a contract associated with an advertisement isunderperforming, that advertisement can be scheduled in a validadvertisement break based on the scoring, in an attempt to improve theperformance of the advertisement in accordance with the contract. Insome embodiments, the advertisement break selected can have the highestratings score based on the plurality of criteria.

Furthermore, the advertisement can be assigned to an advertisement breakbased one or more selectable scheduling parameters, such as a period.Various scheduling tasks can be performed recursively, so that the totalpredicted performance value can be maintained within with a predictedperformance value for the next advertisement. Additionally, someembodiments include generating a proposed advertising schedule based onthe total predicted performance value and the performance contractualvalue.

Various embodiments can be implemented as a computer readable mediumtangibly embodying a program of computer executable instructions.Furthermore, in some instances the technology disclosed herein can beimplemented as a system including memory, and a processor or othercircuitry configured to execute a program of instructions.

BRIEF DESCRIPTION OF THE DRAWINGS

The detailed description will refer to the following drawings, whereinlike numerals may refer to like elements, and wherein:

FIG. 1 is a flowchart illustrating an embodiment of a method forpredicting gross rating points;

FIG. 2 is a flowchart illustrating an embodiment of a method forscheduling advertisements to achieve a contractual level of GRP;

FIG. 3 is a flowchart illustrating an embodiment of a method forgenerating an advertising lay-down to achieve a contractual level of GRPis shown;

FIG. 4 is a flowchart illustrating an embodiment of a method forentering an advertising lay-down into a web browser and estimating theGRP to be achieved from this lay-down in real-time;

FIG. 5 is a diagram illustrating a web-form utilized by an embodiment ofa method for entering an advertising lay-down into a web browser andestimating the GRP to be achieved from this lay-down in real-time; and

FIG. 6 is a diagram illustrating guidance lanes.

DETAILED DESCRIPTION

Described herein include systems, software and methods for predictingand achieving contractual levels of advertising performance. Someembodiments discussed herein predict or achieve contractual levels ofgross rating points (GRP). It is understood that other metrics know tothose of ordinary skill in the art for measuring levels of advertisingperformance may be used in place of GRP in the embodiments describedherein.

A gross rating point can be defined as a unit of measurement ofadvertising audience size equal to one percent of the total potentialaudience universe. It can be utilized to measure the exposure of one ormore programs or commercials without regard to multiple exposure of thesame advertising to individuals. Thus, a GRP can be the product of mediareach multiplied by exposure frequency. Media reach can be defined asthe total number of individual prospects exposed to the advertisementand is usually stated as a percentage of the target market. Exposurefrequency is related to the number of exposures an advertisementreceives in the target market or demographic. The GRP may includeduplicate exposure, where the same listener hears the same advertisementat different times. Generally, a spot is an advertisement having adefined time or other segment of content played on a media outlet suchas a radio station. A GRP metric may not reveal how often each listenerheard the same spot (frequency) or the number of unduplicated listenersthat heard the same spot (reach).

GRP, for a radio station, can indicate the percentage of the populationthat is listening to the advertisement at any instance of time. A GRP ofone (1) represents 1% of a target demographic, and 100.0 GRP represents100% of the demographic. GRP can be calculated for each minute of eachday and there could be audience duplication over the course of acontract. Therefore a GRP of over 100% can be provided for a particularcontract.

Advertisement listener data provided by many monitoring methods andagencies can be utilized to calculate GRP. One such method utilizes aPortable People Meter (PPM) device that provides listening activity datato agencies such as TSN Gallup, Nielson, BBM, etc. A PPM is anelectronic device advertisement is typically worn by members of theradio listening public and it records what a sample group of people arelisting to at any given time. The PPM and other related devices can logand store data regarding radio listenership for each minute of the dayand this data can be uploaded to a monitoring entity such TSN Gallup ona daily basis where the data can be pooled in a central database. Eachperson with a PPM device can be assigned a weighting that relates to thepercentage of the total population that he or she represents. Thisweighting can be from between 0 and 100. The PPM data can then bepurchased by subscribing radio stations for use in accurately trackingthe listening habits of their audience and can provide GRP data. PPMdata can provide minute-by-minute data regarding both the listeninghabits and demographic information of PPM participants.

The system and methods described herein may be implemented as part of asystem that schedules media content such as, by way of example, theAirwaves Traffic software suite that is commercially available fromRadio Computing Services, Inc. Airwaves Traffic software can control thescheduling of advertisements and related invoicing functions, providesales and revenue projection reports, and can manage clients and clientcontacts for multiple stations in multiple time-zones. Airwaves Onlineservices is a web-based service that allows advertising account managersto view inventory of media outlets such as radio stations and availabletime slots, and allows users to track historical performance ofadvertising campaigns and allows users to predict impact (listenership),and to create proposals, to plan campaigns, to schedule and toautomatically implement an “advertising lay-down.” “Advertisinglay-down” can be defined as the scheduling of advertising spots over atime-period (e.g., a day-part, day, days, week, etc.) on one or morestations and/or media outlets.

Airwaves Traffic software is generally a suite of software applicationthat can run on a server component and work station components operatingunder, e.g., a Windows Vista, 2003, 2000, XP, Linux, Mac, Solaris, etc.operating system. In some embodiments, Microsoft SQL Server 2005 is usedto support the Airwaves Traffic software suite databases and MicrosoftOffice and Microsoft Outlook can also be utilized. In some embodiments,the Airwaves Traffic suite can execute code written in MFC (C++)language and the Airwaves Online suite can execute code written in C#language.

The Airwaves Traffic suite can calculate GRP for a past spots, byutilizing impacts and population data for the spot's specificdemographic. Impacts, impressions, or the number of people assumed tohave heard the spot can be divided by the population in range of atransmission, to provide a percentage of PPM listeners matching thedemographic who heard the spot. Impacts generally are the number ofactual listeners estimated to have heard the spot. Impacts can becalculated by multiplying the weighting of each PPM user by 1000. Forexample, if a PPM participant with a weighting of 8.6957 listens to aspot, then it can be estimated that the spot received 8695 impacts basedon that participant hearing the spot. It can be appreciated that thatthe impacts achieved over the course of a contract can include audienceduplication, as the same participant may hear multiple spots for thesame contract. In some embodiments, audience duplication can haveassociated weighting. Research has shown that when that same listenerhears a spot repeatedly it improves the overall effectiveness of thespot or response to an advertisement up to a point, at which pointsaturation is reached. After saturation, the effectiveness can degrade(e.g., hearing once is good, hearing twice is better, hearing threetimes is best, but hearing four times can be a wasted advertisementresources). The weighting based on audience duplication can utilize thisscale and weights impacts for most effective delivery of anadvertisement. Population can be calculated by adding the weightings ofall PPM participants together. In some embodiments, only the PPMparticipants matching the demographic are added together whencalculating GRP for a spot.

With reference now to FIG. 1, a method 100 for predicting impact usinggross rating points (GRP) is illustrated. As illustrated by block 110 aHistoric GRP table is depicted, populated with historical GRP data,which can include data obtained from a consumer survey. In someembodiments, the table can provide actual GRP data (if available) foreach station, demographic, day of week (Mon-Sun), and minute of the day(i.e., 1 to 1440). Historical GRP data can be calculated from dataacquired from actual listening habits of people such as the dataobtained from PPM users. The Historic GRP table can include at leastfive (5) weeks' worth of historical GRP data.

In some embodiments, the Historical GRP data can be filtered, using thedemographic or other information obtained from a consumer survey, togenerate a filtered set of historic data. As used herein, the termHistorical GRP data can include filtered Historic GRP data. Furthermore,multiple demographic filters can be provided, for example, Age+Gender,Age+Car, Age+Shopper, or the like. Some embodiments limit the number ofdemographic filters to two, to ensure a statistically sufficient samplesize and prevent a need for additional sample-size validation.

As illustrated by block 120, a GRP Summary table that can summarize andtrend historical GRP data can be generated by storing one predicted GRPvalue for every combination of, e.g., station, demographic, and day ofweek (Mon to Sun). Trend data based on historical GRP may be determinedby trending algorithms. Some embodiments may use a feature called“guidance lanes,” to determine trends. An example of guidance lanes andtheir usage is shown in FIG. 6 and discussed below. A predicted GRPvalue can be determined by averaging matching historic GRP combinationsfor, e.g., the previous five (5) weeks, for each combination of station,demographic, and day of week. If historic GRP cannot be located,Historic GPR can be excluded from the average. This process can berepeated for every combination of station, demographic, day of the week,etc.

As illustrate by block 130, the Historical GRP table can be updated withmore current GRP data, for example on a daily basis, but in someembodiments at other intervals. For example, the updating can occur on asubstantially continual basis, or be performed periodically at intervalsother than daily. This update feature can be a user selectable feature.After the Historical GRP table is updated as illustrated by block 130, asummary in accordance block 120 can be performed again, i.e., the GRPSummary table is updated with new GRP predictions based on the updatedhistorical data.

The GRP Summary table can be used in preparing a proposal to anadvertiser where the proposal can predict how much GRP will be achievedby a particular series of advertisements. It can be appreciated that insome embodiments performance metrics other than GRP (e.g., WAVE, Impactsor Impressions) may be used to predict and measure impact or advertisingperformance. For example, Bayesian regression, Linear Regression, etc.may be utilized to predict impact or advertising performance, insteadof, or in cooperation with GRP advertisement in addition to station,demographic, day of week, and quarter hour may be utilized to recordhistorical impact and predict impact, and that the look-back period forhistorical data could be more or less than five weeks.

An advertising contract often will designate a “Contractual GRP” value,which is the GRP that the contract is expected to achieve. Thecontractual GRP is normally specified by, or agreed upon between theseller of the inventory and the advertiser. The disclosed system allowsthe advertiser and the seller of the advertising to predict, plan,implement and monitor an advertising campaign for compliance with atarget Contractual GRP value.

“GRP Achieved” for logged and reconciled advertisements can be describedas GRP from the minute the spot went to air. GRP Achieved can be basedon actual data such as PPM data. The GRP provided and/or received viathe contract can be evaluated as the GRP Achieved for each spot subjectto the contract.

“GRP Scheduled” can be defined as future spots or spots that have airedin the past, but have not yet been logged and reconciled. The GRPScheduled can be predicted based on a five week average of GRP Achievedand the GRP Scheduled can be averaged for each quarter hour of each day.The “GRP Estimated” can be defined as the total of GRP Achieved and GRPScheduled for an entire contract. “GRP Difference” can be defined as adifference between the Contractual GRP and the GRP Estimated on aspecific contract. The Airwaves suite of software advertisement can alsotake into account the GRP Difference and can schedule advertisements sothat advertisements in underperforming contracts are moved such thatthey are scheduled in high-GRP spots or receiving breaks. Databasetables or other storage structures can be utilized to store data foreach advertisement and each contract and for the entire system. Eachcontract can have a Contractual GRP value or target associated with it.The disclosed arrangements can also tore a tolerance level, defining anextent to which the GRP Estimated is allowed to deviate from theContractual GRP. It can be appreciated that an advertiser may pay morefor a contract where the level of GRP or level of performance isguaranteed. The tolerance value can also be the same for all GRPcontracts handled by the disclosed arrangements. In some embodiments thetolerance value can be set by default to 10%, and in some embodiments itcan be adjusted higher or lower by a user.

Referring to FIG. 6, guidance lane embodiments are disclosed. A guidancelane can be a combination of correction points and data filtering thatprovides scheduling guidance such that a user can obtain a predicted GRPor a GRP goal. In some embodiments an estimated GRP goal can becalculated and plotted over time (e.g., a trending increase in GRP asshown in FIG. 6). Boundaries such as a Guidance Boundary and OutlierBoundary can be set on both sides of an estimated GRP value or GRP goal.The boundaries can be calculated based on empirical data (e.g., priorratings and delivery data with weighted seasonal and market adjustments)for a particular market and station and can be adjusted. A guidance laneallows for linear regression to be applied to actual GRP values andallows for short-term corrections to adjust actual GRP values fallingwithin a first set of guidance boundaries (the “Guidance Boundaries” inFIG. 6). Such a correction can be referred to as a nudge. Short-termcorrections applied to adjust GRP values falling outside the first setof guidance boundaries but within the second set of guidance boundaries(the “Outlier Boundaries” in FIG. 6), can be referred to as“corrections.” and noise elimination. Corrections and noise eliminationcan ignore actual GRP values (“Outliers”) or values that fall outside ofthe second set of guidance boundaries. A control mechanism forscheduling spots can feed the “Nudges” and “Corrections” to adjust thespot schedules in order to ultimately meet the GRP goal.

With reference now to FIG. 2, a method 200 for scheduling advertisementsto achieve a contractual level of GRP is illustrated. The method 200 canschedule advertisements to achieve a Contractual GRP value or can beutilized to schedule a particular date and station combination.

As illustrated by block 210, a station and date combination can beselected for an advertisement.

As illustrated by block 220, advertisements for the station and datecombination can be sorted into an ordered list. The ordering, asillustrated by block 220, can be determined by a GRP Difference and atolerance percentage for each advertisement. Advertisements whichreceive a highest ranking can be advertisements whose GRP Differencediffers from a Contractual GRP by more than the tolerance percentage.The highest ranking advertisement can be the advertisement with thegreatest GRP Difference.

The advertisements in the ordered list can be scheduled one at a time,beginning with the advertisement with the highest ranking. Asillustrated by block 230, the highest-ranking unscheduled advertisementcan be selected for scheduling.

Validity criteria rules that specify a validity of schedule placementcan be associated with each advertisement spot. Some validity criteriacan include day-part rules, i.e. what time during the day anadvertisement could be played, and rules to prevent advertisementclashing, a mechanism to avoid situations where advertisements forsimilar or competing products run to close each other for example in thesame advertisement break. Overbooking rules can also be applied whereadvertising breaks can have parameters or settings that dictate amaximum duration and maximum number of spots. Thus, attempting to placea spot in a break that will or would not fit can cause overbooking (ifone or more of the maximums are exceeded or conditions are not met).Day-part rules, overbooking and clashing can cause an invalidity flagand message to be generated advertisement. As illustrated by block 240,a collection of valid advertisement breaks can be formed by collectingadvertisement breaks which are valid or alternatively “not invalid”based on validity criteria and/or previously-scheduled advertisements.

The database table associated with the advertisement can include GRPhistorical data about the advertisement including all previous dates andtimes each station has run the advertisement and the actual GRPachieved. As illustrated by block 250, each advertisement break in thecollection of valid advertisement breaks can be scored according to howoften the advertisement has played in a specific break in the past.Higher scores are typically achieved to breaks and/or day parts in whichthe advertisement has not previously run.

Two values can be related to and possibly stored with eachadvertisement, the GRP Achieved and the GRP Scheduled. In someembodiments, only one of these values will be filled out. If theadvertisement has played and its actual aired time has been reconciledthe GRP achieved value can be set, otherwise the GRP Scheduled value canbe set. The total GRP Achieved and GRP Scheduled may also be stored atthe contract level, which can be a sum of the GRP Achieved and GRPScheduled values for all advertisements for a contract.

As illustrated by block 260, the GRP Estimated for an advertisement canbe evaluated to determine if the contract is underperforming. If thecontract is underperforming, e.g. if the GRP Achieved is lower than theContractual GRP by more than the tolerance percentage, scores for theadvertisement breaks can be further adjusted as illustrated by block270. If the contract is not underperforming, the method can proceed toblock 280.

If the system is not performing in accordance with the contractualrequirements or the media outlet is underperforming then as illustratedin block 270 the score of each advertisement break can be adjusted by avalue based on the predicted GRP that would achieved the contractualobligation. The adjustment could be made by scheduling the advertisementin advertisement breaks that will likely achieve a higher GRP. Thepredicted GRP can be determined in accordance with FIG. 1. The scores ofthe advertisement breaks can be adjusted so that a higher predicted GRPcan lead to a higher score for the advertisement break. Further theadvertisement break with the highest predicted GRP can have the highestscore.

As illustrated by block 280, the advertisement can be scheduled to runin the advertisement break a high, or the highest, score. As illustratedby block 290 the system can check to see whether unscheduledadvertisements slots remain for the selected day and station. If spotsremain the process can return to block 230 and if no spots remain theprocess can end. Underperforming contracts can be automaticallycorrected if they are not achieving the contractual level of GRP.

In some embodiments, scheduling can determine advertising campaigns thatthat are over-performing in accordance with their contract, where thatGRP Estimated for the contract exceeds the contractual GRP by more thanthe tolerance percentage. In some embodiments, advertisements that arebeing scheduled for an over-performing GRP can be rescheduled, leavingopen spots for use by underperforming campaigns such thatunderperformers can increase their GRP. Compensating for over performingcontracts ensures that inventory is not wasted and more contractualrequirements are fulfilled. The method described can be adapted to useimpacts other than GRP.

The Airwaves system permits a user to specify a contractual GRP valuefor an advertising lay-down, the demographic, stations and month thelay-down is to occur in, and weightings that determine howadvertisements are to be spread across week-days, week-ends andday-parts, and the system can automatically generate an advertisinglay-down that achieves the contractual GRP value.

With reference now to FIG. 3, a method 300 for generating an advertisinglay-down to achieve a desired level of GRP is shown.

As illustrated by block 310, the user can select parameters, including atotal GRP desired for the proposal, the target demographic, the stationswhich will air the advertisements and the month (or other time period),day of week distribution and day part distribution in which theadvertisements will run. In some embodiments the user can also selectweightings that determine how advertisements are to be distributedacross days of the week and times of the day. In some embodiments, thereare two sets of weightings:

Across days of the week:

These weightings default to preset values:

Weekday (Mon-Fri): 76%

Weekend (Sat-Sun): 24%; and

Across times of the day:

The weightings can be stored on day parts which are completely userdefinable. A typical set of day parts and weightings follows:

Daytime (06:00:00-17:59:59) 80%

Evening (18:00:00-21:59:59) 11%

Late Night (22:00:00-23:59:59) 2.25%

Early Morning (00:00:00-05:59:59) 6.75%

Based on these parameters and predicted GRP summary data a proposedadvertising lay-down can be created as described below.

As illustrated by block 320, an advertisement can be randomly assignedto an available combination of station, day and time-zone (which in GRPselling can be a sub-set of a day-part). The random assignment can beweighted according to the weighting percentages described above. Therandom assignment can also take into consideration remaining inventoryso that advertisements can avoid being placed in areas of the day thathave no available inventory. Time-zone can be defined as a time specificselling zone. Time-zone usually conforms to a day-part, although a usercan set up time-zones that are subsets of or cross multiple day-parts.

As illustrated by block 330 the predicted GRP value can be computed forthe advertisement assigned as illustrated by block 320. Such anassignment can be accomplished in accordance with the method describedwith references to FIG. 1.

The predicted GRP values for assigned advertisements can be summed intoa Total Predicted GRP, which can be compared to the desired GRP value asillustrated by block 340. If the Total Predicted GRP is less than thecontractual GRP value, the process can return to block 320 to assignanother spot. Otherwise, as illustrated by block 350 the process cangenerate a proposal containing an advertising laydown that satisfies thetotal GRP contractual desires and the process can end thereafter.

The Airwaves Online software suite can be implemented as a web-basedapplication that allows a user to enter a proposed advertising lay-downon a web-based form and with little time delay that user can see thepredicted GRP of the proposed lay-down. The lay-down can be for aspecific demographic and month and cover multiple radio stations.

With reference now to FIG. 4, a method for entering an advertisinglay-down into a web browser and for receiving an estimate on GRP basedon the laydown in “real-time” is shown. As illustrated by block 410, aweb page containing a web-form is provided, preferably by a web server.Referring briefly to FIG. 5 a web-based-form 500 is illustrated thatcould be utilized to enter an advertising lay-down into a web browser.The web-based form could also be utilized to provide an estimated GRPthat corresponds to the lay-down that was entered by the user. Web-form500 can correspond to a selected month and demographic and multiplemedia outlets or radio stations. Web-Form 500 can include row headings520 corresponding to each time-zone, and column headings 510corresponding to each day of the month. Web-based form 500 can alsoinclude columns which can provide a subtotal or entries for a particulartime-zone and for the entire month: column 530, can subtotal the numberof spots per time-zone; column 540, can subtotals the advertising rate;and column 550, can subtotal the GRP for the time-zone across the entiremonth. Subtotal 570 can sum up all GRP values in column 550 to show thetotal predicted GRP for the entire proposed lay-down. The cell at theintersection of each row 520 and column 510 can refer to the advertisingspots in a particular time-zone and day of the month.

Returning to FIG. 4, and as illustrated by block 420 a cell can bepopulated with a number corresponding to the number of advertisementsthat will run on the selected day and in the selected time-zone. Thecell entry can be the number of advertisements to be placed for eachstation. Accordingly, if three stations are selected and a two isentered into the cell, six advertisements will be placed. The cell canbe populated by a web server based on input provided by a user,preferably via conventional web-communication protocols such as HTTP andHTML over the Internet.

As illustrated by block 430, the GRP subtotal value in column 560 forall GRP values in the time-zone selected by the user can be updatedautomatically in near real time to reflect the additional GRP providedby the proposed advertisements. As illustrated by block 440, the totalGRP for the entire proposed lay-down can be updated automatically innear real time and displayed in cell 570.

In some embodiments, web-based-form 500 could have a selectable tabwhich could provide a GRP to be gained by placing a spot in a specifiedcell. The web-form shown 500 does not specifically illustrate what GRPis predicted by placing an advertisement in a specific cell. Additionaltabs could enable a user to see which areas of the day are high-yieldingGRP areas for certain parameters. The web-form could also be adapted touse impacts or listener based data other than GRP.

Various disclosed embodiments can be implemented in hardware, software,or a combination containing both hardware and software elements. In oneor more embodiments, the invention is implemented in software, whichincludes but is not limited to firmware, resident software, microcode,etc. Some embodiments may be realized as a computer program product, andmay be implemented as a computer-usable or computer-readable mediumembodying program code for use by, or in connection with, a computer, aprocessor, or other suitable instruction execution system.

By way of example, and not limitation, computer readable media maycomprise any of various types of computer storage media, includingvolatile and non-volatile, removable and non-removable media implementedin any suitable method or technology for storage of information such ascomputer readable instructions, data structures, program modules, orother data. Computer storage media include, but are not limited to, RAM,ROM, EEPROM, flash memory or other memory technology, CD-ROM, digitalversatile disks (DVD) or other optical storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or any other medium which can be used to store the desired informationand which can be accessed by a computer. Data structures andtransmission of data (including wireless transmission) particular toaspects of the disclosure are also encompassed within the scope of thedisclosure.

The terms and descriptions used herein are set forth by way ofillustration only and are not meant as limitations. Those skilled in theart will recognize that many variations are possible within the spiritand scope of the invention as defined in the following claims, and theirequivalents, in which all terms are to be understood in their broadestpossible sense unless otherwise indicated.

What is claimed is:
 1. A method for implementation in an automated mediacontent scheduling system including a memory and processor programmed toimplement the method, the method comprising: obtaining, at the automatedmedia content scheduling system, a target value of a performance metricassociated with an advertising schedule; determining, at the automatedmedia content scheduling system, an upper boundary value indicating avalue of the performance metric at which corrective action to decrease apredicted value of the performance metric is to be taken; determining,at the automated media content scheduling system, a lower boundary valueindicating a value of the performance metric at which corrective actionto increase the predicted value of the performance metric is to betaken; receiving, at the automated media content scheduling system,information indicating the predicted value of the performance metric;and continually adjusting an advertising schedule to maintain thepredicted value of the performance metric between the upper boundaryvalue and the lower boundary value.
 2. The method of claim 1, wherein atleast one of the upper boundary value and the lower boundary value is anoutlier boundary.
 3. The method of claim 1, wherein at least one of theupper boundary value and the lower boundary value is a guidanceboundary.
 4. The method of claim 1, wherein the performance metric is agross rating point (GRP) metric.
 5. The method of claim 4, furthercomprising: generating a plurality of predicted gross rating point (GRP)values for an advertisement by filtering historic data using multipledifferent sets of parameters; and continually adjusting the advertisingschedule to maintain at least one of the predicted GRP values betweenthe upper boundary value and the lower boundary value.
 6. The method ofclaim 5, further comprising: generating a predicted GRP trend line basedon the plurality of predicted GRP values; and continually adjusting theadvertising schedule to maintain the predicted GRP trend line betweenthe upper boundary value and the lower boundary value.
 7. The method ofclaim 1, further comprising: determining a relative rating of aplurality of spot breaks, within a particular market, based on apredicted value of the performance metric associated with individualspot breaks; and continually adjusting the advertising schedule bymoving an advertisement from a first spot break to a second spot breakbased on the relative rating of the first spot break and the second spotbreak.
 8. An automated media content scheduling system comprising: aprocessor; a memory coupled to the processor; a program of instructionsstored in the memory and configured to be executed by the processor, theprogram of instructions including: at least one instruction to obtain atarget value of a performance metric associated with an advertisingschedule; at least one instruction to determine an upper boundary valueindicating a value of the performance metric at which corrective actionto decrease a predicted value of the performance metric is to be taken;at least one instruction to determine a lower boundary value indicatinga value of the performance metric at which corrective action to increasethe predicted value of the performance metric is to be taken; at leastone instruction to receive information indicating the predicted value ofthe performance metric; and at least one instruction to continuallyadjust an advertising schedule to maintain the predicted value of theperformance metric between the upper boundary value and the lowerboundary value.
 9. The automated media content scheduling system ofclaim 8, wherein at least one of the upper boundary value and the lowerboundary value is an outlier boundary.
 10. The automated media contentscheduling system of claim 8, wherein at least one of the upper boundaryvalue and the lower boundary value is a guidance boundary.
 11. Theautomated media content scheduling system of claim 8, wherein theperformance metric is a gross rating point (GRP) metric.
 12. Theautomated media content scheduling system of claim 11, furthercomprising: at least one instruction to generate a plurality ofpredicted gross rating point (GRP) values for an advertisement byfiltering historic data using multiple different sets of parameters; andat least one instruction to continually adjust the advertising scheduleto maintain at least one of the predicted GRP values between the upperboundary value and the lower boundary value.
 13. The automated mediacontent scheduling system of claim 12, further comprising: at least oneinstruction to generate a predicted GRP trend line based on theplurality of predicted GRP values; and at least one instruction tocontinually adjust the advertising schedule to maintain the predictedGRP trend line between the upper boundary value and the lower boundaryvalue.
 14. The automated media content scheduling system of claim 8,further comprising: at least one instruction to determine a relativerating of a plurality of spot breaks, within a particular market, basedon a predicted value of the performance metric associated withindividual spot breaks; and at least one instruction to continuallyadjust the advertising schedule by moving an advertisement from a firstspot break to a second spot break based on the relative rating of thefirst spot break and the second spot break.
 15. A non-transitorycomputer readable medium tangibly embodying a program of instructionsconfigured to be executed by a processor, the program of instructionsincluding: at least one instruction to obtain a target value of aperformance metric associated with an advertising schedule; at least oneinstruction to determine an upper boundary value indicating a value ofthe performance metric at which corrective action to decrease apredicted value of the performance metric is to be taken; at least oneinstruction to determine a lower boundary value indicating a value ofthe performance metric at which corrective action to increase thepredicted value of the performance metric is to be taken; at least oneinstruction to receive information indicating the predicted value of theperformance metric; and at least one instruction to continually adjustan advertising schedule to maintain the predicted value of theperformance metric between the upper boundary value and the lowerboundary value.
 16. The non-transitory computer readable medium of claim15, wherein at least one of the upper boundary value and the lowerboundary value is an outlier boundary.
 17. The non-transitory computerreadable medium of claim 15, wherein at least one of the upper boundaryvalue and the lower boundary value is a guidance boundary.
 18. Thenon-transitory computer readable medium of claim 15, further comprising:at least one instruction to generate a plurality of predicted values ofthe performance metric for an advertisement by filtering historic datausing multiple different sets of parameters; and at least oneinstruction to continually adjust the advertising schedule to maintainat least one of the plurality of predicted values of the performancemetric between the upper boundary value and the lower boundary value.19. The non-transitory computer readable medium of claim 18, furthercomprising: at least one instruction to generate a trend line based onthe plurality of predicted values of the performance metric; and atleast one instruction to continually adjust the advertising schedule tomaintain the trend line between the upper boundary value and the lowerboundary value.
 20. The non-transitory computer readable medium of claim15, further comprising: at least one instruction to determine a relativerating of a plurality of spot breaks, within a particular market, basedon a predicted value of the performance metric associated withindividual spot breaks; and at least one instruction to continuallyadjust the advertising schedule by moving an advertisement from a firstspot break to a second spot break based on the relative rating of thefirst spot break and the second spot break.